1.

INTRODUCTION

Meteorological data are extensively used in research purposes in different sectors including agriculture and economy. For instance, they can be used for crop yield and hydrological monitoring and forecasting, for the assessment of economic impacts of climate change and to assess the impacts of extreme weather events.

After the 2008 crisis^1,2, food security has become one of the highest priorities in the European Commission. European Union policies aim at ensuring the European Union independence from price volatility. In the Joint Research Centre (JRC), the Monitoring Agricultural Resources Unit (MARS) serves the agriculture and food policies of the European Union, their impact on rural economies and on the environment, encompassing the global issues of food security and climate change. A large set of expertise includes crop modeling, agro-meteorology, sampling methods, environmental geo-spatial analysis, econometrics through European and global data infrastructures. Based on this skill-set, JRC generates evidence-based information and forecasts for the management of agricultural practices and early warnings on Food Security.

In this framework, the Crop Growth Monitoring System (CGMS) has been developed. It encompasses remote sensing, meteorological observations, agro-meteorological modelling, and statistical analysis. The results of the crop growth monitoring activities are synthesized in the ‘MARS Bulletin – Crop Monitoring in Europe’³, a monthly report published since 1975.

The Crop Growth Monitoring System (CGMS) provides reliable and timely spatial information about crop status in Europe. The CGMS includes three components:

The objective of this paper is to present the meteorological datasets developed and maintained by the MARS Unit to evaluate its use through a survey, to enhance its distribution, visibility and usability.

2.

DATA

At European level, the Crop Growth Monitoring System (CGMS) manages the meteorological, crop yield and crop area data since 1975. The spatial coverage includes EU Member States, neighboring European countries, candidate countries and the Mediterranean countries (North Africa). More specifically, the meteorological data have been collected from a network of around 5.000 meteorological weather stations throughout Europe.

The original station (point) data are interpolated to produce raster maps with a resolution of 25km x 25km grid cell. The raster datasets are also aggregated to different administrative levels (Countries, regions, provinces), such as NUTS⁴, Nomenclature of territorial units for statistics. The CGMS includes the following meteorological attributes at 25km x 25km grid cells:

Figure 1.

Maximum daily air temperature reached during summer 2013.

Figure 2.

Summer rainfall sums in 2013.

Other derived meteorological variables can be extracted from CGMS, such as daily evaporation and evapotranspiration. The daily evapotranspiration is calculated on the Penman-Monteith method⁵ and represents daily sum of evaporation and plant transpiration.

3.

DISSEMINATION OF INTERPOLATED METEOROLOGICAL DATA

4.

USER EVALUATION SURVEY

4.2

Survey outcome

The survey was sent to the 395 users that actually downloaded data since 2011. Here, we report the analysis of the survey questions:

Interpolated meteorological data served mainly the research community (77%). On a good basis, interpolated meteorological data served Consulting (19%), Education (13%), Policy Support (12%), and Business (10%) (Fig.3).

Figure 3.

Main Purpose of Interpolated Meteorological Data

Interpolated meteorological data have strongly been used both as a main and as an additional source of information, 52% and 56% respectively. Interpolated meteorological data have been also used in validation (16%) and comparison (13%) studies (Fig.4).

Figure 4.

Use of Interpolated Meteorological Data

Interpolated meteorological data proved to meet the expectations of the users, as 92% of the users have been able to use them for the intended purpose. Still, 8% of the user community re-used the information for other purposes. Indeed, 69% of the users rated the data very useful, adding 21% of useful rating. “Somewhat Useless” and “Useless” rating hardly reach 5% together. Same rating for ‘Neutral’ (Fig.5).

Figure 5.

Usefulness of Interpolated Meteorological Data

An interesting output of the survey was that 26% of the users (Fig.6) have used the data for producing publications (papers, posters, conference proceedings). As the visibility of the service, most of the users (51%) were recommended by colleagues, 18% discovered the data through the JRC web pages, 16% contacted JRC staff directly, and 6% learnt of the data through JRC events (Fig.7). Only 9% were able to discover the service through sources other than JRC.

Figure 6.

Publications based on Interpolated Meteorological Data

Figure 7.

Access to the data

The user community encourages the continuation of the service and highlights the importance of the meteorological data especially for scientific studies. A very important feedback is the user appreciation for free data distribution.

On the other hand, users badly judged the procedure to get data through long and inefficient paper-based authorization process. Thus, the new data portal adopts an open access policy that will avoid unnecessary administrative burden for the users. A direct online registration procedure is expected to increase the number of users actually downloading data and to decrease the number of discouraged users. Authorization requests, though online, will be limited to restricted resources such as the heating and cooling degree days. Another useful feedback from users is the lack of detailed documentation for the methods of interpolation and description of the weather parameter. Finally, users suggested to implement a better dissemination strategy and increase the visibility of the current web platform.

5.

METEOROLOGICAL DATA AND POLICY SUPPORT

The analysis of the projects supported by the Interpolated Meteorological Data shows that meteorological data are highly relevant for the development, implementation and assessment of a number of EU policy areas: agriculture, soil protection, environment, agriculture, food security, energy, climate change, health and sustainable development. Specific examples include:

Soil Protection: Temperature and precipitation data are included in models for soil organic carbon estimation at 0-30 cm⁷. The rainfall data are considered as an important input factor for the estimation of erosion by water both in the past application such as PESERA⁸ and in recent developments such as RUSLE and G2⁹ model. Wind speed is considered as main input factor for the qualitative wind erosion models¹⁰.

Food security: The relative humidity data are used by the European Food Security Authority (EFSA) for assessment of pest risk. In research activities, precipitation and temperature data are used for the groundwater risk assessment for pesticides and pesticides exposure. Modelling activities have also used precipitation and temperature data for estimating plant health and growth at regional and European applications¹¹. The most important use of meteorological data is the development of yield forecast at European scale implemented at the JRC and published through the MARS Bulletin³. Based on the current precipitation and temperature data and using past trends and statistical models, scientists can predict the crop growth and production of 18 crops.

Environment: Modelling activities use the rainfall, humidity and temperature data for the development of biomass production indicators, fire risk indexes, flood alerts¹², drought monitor data¹³, soil moisture datasets and air quality indicators.

Agriculture: The rainfall data allow modellers to run scenarios about specific plant growth^11,14 (e.g. Citrus, cotton, maize, vegetable oil) and production in various regions of Europe. Moreover, the meteorological data have been used in genomic selection framework¹⁵ of certain crops.

Energy: The datasets on heating degree days are used in the energy sector for estimating the heat recovery ventilation against natural ventilation¹⁶. The same dataset and the temperature data have been used by gas market.

Climate change: The meteorological datasets are time-series of more than 30 years. Those data have been also used in climate change modelling in detecting trends and identifying projections of rainfall decrease and temperature increase¹⁷. The impact of climate change to certain crops has also been investigated¹⁸.

Research & Development: numerous of EU research projects in 6^th and 7^th framework programme (e.g. DESIRE, SEAMLESS, PRATIQUE, CARBOEUROPE, CIRCE, HAIR, GEOLAND-2, FUME) have requested the available datasets as input for their modelling activities.

Health: The data are useful input for exposure assessments¹⁹ and forecasting seasonal diseases such as malaria, blue tongue.

Water management and water protection: The meteorological data have been used in the implementation of scenarios for future water usage in Europe.

6.

CONCLUSIONS

The European Meteorological datasets developed by Joint Research Centre (JRC) have been proved to be a useful source of information for research and modelling purposes, as revealed by a dedicated user survey. Free availability, daily updates, the temporal coverage (more than 30 years) as well as the spatial coverage (European Union, neighboring countries and North Africa) are significant advantages of this product that has been already used in several different research fields, e.g.: climate change, food security, sustainable growth and agriculture.